An online Decision Support System for Recommending an Alternative Path of Execution

Traditional process execution follow the path of execution drawn by the process analyst without observing the behaviour of resource and other real time constraints. Identifying process model, predicting the behaviour of resource and recommending the optimal path of execution for a real time process is challenging. The proposed AlfyMiner: (y)Miner gives a new dimension in process execution with the novel techniques Process Model Analyser: PMA(Miner) and Resource behaviour Analyser: RBA(Miner) for recommending probable path of execution. PMA(Miner) discovers next probable activity for currently executing activity in an online process using variant matching technique for identify the set of next probable activity, among which the next probable activity is discovered using decision tree model. RBA(Miner) identifies the resource suitable for performing the discovered next probable activity and observe the behaviour based on; load and performance using polynomial regression model, and waiting time using queueing theory. Based on the observed behaviour yMiner recommend the probable path of execution with; next probable activity and the best suitable resource for performing it. Experiments were conducted on process logs of CoSeLoG Project(1) and 72% of accuracy is obtained in identifying and recommending next probable activity and the efficiency of resource performance was optimized by 59% by decreasing their load.